There is a growing consensus that technology growth has been the primary driving force in rising health care costs and has yielded great benefits in functioning and longevity. Yet our understanding of technology growth in health care is rudimentary. For example, why do some regions rapidly adopt highly efficient treatments such as beta blockers for heart attacks (well-established improvements at very low cost), or less efficient treatments such as spinal fusion with instrumentation (small or unknown improvements at higher cost)? Social networks are likely important, but there is minimal understanding of how the environment in which physicians work affects adoption of efficient innovations. Finally, do elderly people in the Health and Retirement Study (MRS) with more efficient health care providers actually experience better outcomes? These questions will be addressed in our 5 specific aims that: 1. Measure what factors have contributed to costs in the Medicare population during the last several decades. Is "technological progress" primarily new procedures, diffusion across wider population groups, or greater frequency of use for the same patients, and to what extent is "hi-tech" really high-cost? 2. Develop measures of network referral "density" from the Medicare claims data. With these measures and with physician certification scores, study the adoption of efficient drug prescription methods using physicianlevel drug prescription data from two states and hospital-level data on beta blocker use from 1994-1995 and 2004-2005. 3. Estimate factors associated with the diffusion of high-cost surgical innovations, particularly those whose efficiency may vary across population groups or providers. We consider angioplasty and bypass surgery for heart attacks, carotid endarterectomy, and spinal fusion. 4. Study the diffusion of new technologies to the oldest-old, including procedures such as cardiac surgery and carotid endarterectomies. How does the marginal effectiveness of these treatments differ from the younger-old, and across regions? 5. Match hospital and provider quality information derived from Medicare claims and other sources data as measured in Project 1 to individual data in the HRS (either by zip code or by individual identifier) to test the hypothesis that the efficiency of health care providers affects the long-term dynamics of health transitions in the general elderly population.